Statistical and Numerical Robust State Estimator for Heavily Loaded Power Systems

Abstract

Real-time state information provided by the state estimator plays a major role in power system monitoring and control. As a result, the convergence of the estimator under various system operating conditions becomes one of the key requirements. In this paper, a state estimation framework that achieves both statistical and numerical robustness is proposed. This estimation framework also generalizes several well-known estimators, including the weighted least squares estimator, the least absolute value estimator, the Huber Maximum-likelihood-estimator, and the Schweppe-type Huber generalized Maximum-likelihood (SHGM)-estimator. The statistical robustness of each estimator has been studied analytically through the total influence function. In addition, the dependence between the statistical robustness of an estimator and the numerical robustness of the iterative algorithm is investigated. To enhance the numerical robustness of the iterative algorithm that solves the SHGM-estimator, a fourth-order Levenberg–Marquardt approach-based SHGM (LM-SHGM) estimator is proposed. It integrates the statistical robustness of that estimator to address various types of bad data and the numerical robustness of the LM approach to handle highly stressed operating conditions. Numerical results carried out on the IEEE test systems demonstrate the good convergence and robustness of the proposed approach under various operating conditions.